Accounting-Based Valuation and Predictability of Stock Market Returns: A Re-Examination

Posted: 18 Nov 2019

See all articles by Jing Fang

Jing Fang

Hong Kong Polytechnic University

Date Written: November 7, 2019

Abstract

Using monthly data from 01/1985 to 12/2012, we find that the accounting valuation-based predictor introduced in Lee, Myers, and Swaminathan (1999) has excellent in-sample and out-of-sample predictive performance. Our finding suggests that the accounting valuation-based predictor does not suffer the problem of instable in-sample and poor out-of-sample performance that Welch and Goyal (2008) document with a long list of predictors suggested by the academic literature. Moreover, we find that forecasts based on widely-used valuation ratios and business cycle variables do not encompass forecasts based on the accounting valuation-based predictor, suggesting that the accounting valuation-based predictor carries information not captured by these valuation ratios and business cycle variables. Furthermore, in line with Lee et al.’s (1999) reasoning that the predictive power of the accounting valuation-based predictor stems from its ability to capture market-wide mispricing, we find that contemporaneous investor sentiment and expectations account for a considerable proportion of the variance of the accounting valuation-based predictor. Consistent with Lee et al.’s (1999) observation, we provide further evidence that using time-varying industry-specific discount rates based on short-term T-bill rates and analyst forecasts to estimate the intrinsic value of equity is essential to the success of the accounting valuation-based predictor in predicting future market returns.

Keywords: Accounting-based valuation, Stock market return predictability, Out-of-sample performance, Mispricing

JEL Classification: G02, G12, G14, M41

Suggested Citation

Fang, Jing, Accounting-Based Valuation and Predictability of Stock Market Returns: A Re-Examination (November 7, 2019). Available at SSRN: https://ssrn.com/abstract=3482423

Jing Fang (Contact Author)

Hong Kong Polytechnic University ( email )

11 Yuk Choi Rd
Hung Hom
Hong Kong

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